Uncertainty propagation in a global biogeochemical model driven by leaf area data

Abstract

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Satellite-observed leaf area index (LAI) is often used to depict vegetation canopy structure and photosynthesis processes in terrestrial biogeochemical models. However, it remains unclear how the uncertainty of LAI among different satellite products propagates to the modeling of carbon (C), nitrogen (N), and phosphorus (P) cycles. Here, we separately drive a global biogeochemical model by three satellite-derived LAI products (i.e., GIMMS LAI3g, GLASS, and GLOBMAP) from 1982 to 2011. Using a traceability analysis, we explored the propagation of LAI-driven uncertainty to modeled C, N, and P storage among different biomes. The results showed that the data uncertainty of LAI was more considerable in the tropics than in non-tropical regions, whereas the modeling uncertainty of C, N, and P stocks showed a contrasting biogeographic pattern. The spread of simulated C, N, and P storage derived by different LAI datasets resulted from assimilation rates of elements in shrubland and C3 grassland but from the element residence time (τ) in deciduous needle leaf forest and tundra regions. Moreover, the assimilation rates of elements are the main contributing factor, with 67.6, 93.2, and 93% of vegetated grids for the modeled uncertainty of C, N, and P storage among the three simulations. We further traced the variations in τ to baseline residence times of different elements and the environmental scalars. These findings indicate that the data uncertainty of plant leaf traits can propagate to ecosystem processes in global biogeochemical models, especially in non-tropical forests.

Keywords

• ecosystem modeling
• leaf area index
• nitrogen cycle
• phosphorus cycle
• traceability analysis
• uncertainty propagation